CognitiveDog：視覚と言語を四足歩行ロボットの行動に変換する大規模マルチモーダルモデルシステム
CognitiveDog: Large Multimodal Model Based System to Translate Vision and Language into Action of Quadruped Robot
大規模マルチモーダルモデルを搭載した四足歩行ロボットが、自然言語の指示と環境の手がかりから自律的に物体を操作・運搬し、実行中に言語で状況を説明するシステムを開発した。
著者: Artem Lykov, Mikhail Litvinov, Mikhail Konenkov, Rinat Prochii, Nikita Burtsev, Ali Alridha Abdulkarim, Artem Bazhenov, Vladimir Berman, Dzmitry Tsetserukou
分類: cs.RO
原文アブストラクト
This paper introduces CognitiveDog, a pioneering development of quadruped robot with Large Multi-modal Model (LMM) that is capable of not only communicating with humans verbally but also physically interacting with the environment through object manipulation. The system was realized on Unitree Go1 robot-dog equipped with a custom gripper and demonstrated autonomous decision-making capabilities, independently determining the most appropriate actions and interactions with various objects to fulfill user-defined tasks. These tasks do not necessarily include direct instructions, challenging the robot to comprehend and execute them based on natural language input and environmental cues. The paper delves into the intricacies of this system, dataset characteristics, and the software architecture. Key to this development is the robot's proficiency in navigating space using Visual-SLAM, effectively manipulating and transporting objects, and providing insightful natural language commentary during task execution. Experimental results highlight the robot's advanced task comprehension and adaptability, underscoring its potential in real-world applications. The dataset used to fine-tune the robot-dog behavior generation model is provided at the following link: huggingface.co/datasets/ArtemLykov/CognitiveDog_dataset
